3.1 Statistical description and correlation analysis
A considerable quantity of sample collection and concoct is prudence to explicate the deviations which may arise due to higher degree and complexity in the chemical and nutritional quality of peanut samples (Srivastava, Mishra, and Mishra 2018a). A total of 150 peanut samples were observed for the change in moisture, FFA, PV value, fat and protein content.
3.2 Elucidation of FTNIR Spectral Attributes for quality of peanut
The overall spectrum range of 12500 to 4000 per cm was noticed after complete scanning of the different peanut samples. Figure 1 shows spectra of different peanut samples with major peaks at wavenumbers 53.0853, 4954.98,4464.03, 4070.85, 74.75.63, 8230.21, and 6178.13 in per cm. First and second derivate mathematical preprocessing was also applied in order to eliminate multiple baseline for different chemical quality parameters of peanut. Interpretation of spectral characteristics is of importance for the identification of the information region, which shall help in the model development via FTNIR analysis (Srivastava et al. 2018 a,b). The fresh peanut samples are represented by pink line spectra, partial roasted by red line spectra, roasted by yellow line spectra, mature by black line spectra, long time stored by light green line spectra, rancid by violet line spectra, fungus infested by light blue line spectra, and damaged by dark blue line spectra respectively (Fig. 1).
The FTNIR spectra originated was normally distinctive for every peanut samples. The higher absorbance spectra specified the damaged (1.3 AU), fungus infested (0.92 AU), and rancid (0.73 AU) peanut samples while the lower absorbance spectra specified the fresh (0.31 AU), partial roasted (0.34 AU), and roasted (0.48 AU) peanut samples. The mature (0.56 AU) and longtime stored (0.69 AU) peanut samples lied in the midway of the two (higher/lower) absorbance spectra (Srivastava et al. 2018 a, c). The water combination (OH stretch. + OH bend) absorbance band due to moisture content of the peanut sample was found at 5212 cm− 1. The absorbance band range 5180 to 5150 cm− 1 revealed the symmetrical stretching and bending of –OH group, while the asymmetrical stretching was found at 7270 to 7220 cm− 1 (Mishra et al. 2018). The bound –OH functional group of first and second overtones lied in the range of 6927 to 6380 cm− 1 and 9408 to 9359 cm− 1 respectively. The N-H stretching and N-H bend of the amide group for the protein content of the peanut were noticed at 5552 and 4843 cm− 1 respectively. The spectral band at 5239 cm− 1 and 4954 cm− 1 was due to the carboxylic acids in the second and first overtone stretch which corresponds to the FFA content of the peanut (Adewale et al. 2014). The peroxide bonds were observed at 4070, 4273, and 5308 cm− 1 due to R-O-R stretch.
3.3 PLS Model for Quality Characteristics of peanut
The Beer’s law holds a specific application in the empirical regression as a correlation accord between peanut category and the spectral signatures of it. The PLSR was employed for the quantification and characterization of the peanut samples by integrating the spectral characteristics in the OPUS-QUANT 5.5 software tool of the instrument (Srivastava et al. 2018 a, Chen et al. 2014). The moisture content, protein, fat, FFA and PV values gave good performance and high correlation for the calibration and validation values in the different peanut samples. The linear regression graphs of calibration (a) and cross validation (b) for the moisture content, protein, fat, FFA, and PV value are shown in Figs. 2–6 respectively. The fitting accuracy along with PLS factors, R2, RMSECV, and RMSEE of the calibration and cross validation for the moisture content, fat, protein, FFA and PV values in the peanut samples is represented in the Table 1.
The accuracy and suitability of a model is signified through its preprocessing treatment selection with high R2 and minimum RMSECV, RMSEE values for the validation and calibration data set respectively. The rank in the regression graphs called as the PLS factors is based on the number of correct factors chosen to establish variation between the different peanut samples and the spectrum bands of them. The FFA had the lowest value of calibration and validation errors (0.579 and 0.738) followed by the protein (0.736 and 0.765), moisture content (0.69 and 0.90), PV value (0.84 and 1.36), and fat content (1.44 and 2.22) respectively.
A PLS factor of 5 for FFA, and moisture content signified that these variables were used for validating the model (Szigedi et al. 2011; Srivastava et al. 2018 a, c). A higher R2 with lower calibration and validation errors with 1 PLS factor for protein was observed to be in accordance with studies appraising proteins of other oilseeds (Velasco and Becker 1998). Thus, the higher value of R2 at minimum value of the RMSEE show the accuracy and precision of the FTNIR model developed. The model developed found to be in line with the previously developed model for rapeseed and soybean (Ferreira et al. 2014). These findings highlight the usefulness of FTNIR in detecting the multiple components in a peanut seed in a non-destructive way and found to be helpful in determining the quality of peanut.
Table 1
Different statistical values during validation and calibration for various response variables utilizing different spectral preprocessing techniques in FT-NIR models development.
Response
variables
|
Wavelength bandwidth
|
Spectral preprocessing (Mathematical)
|
PLS
factor
|
Validation
|
Calibration
|
|
|
|
|
RMSECVx
|
R2
|
RMSEEy
|
R2
|
Moisture content
|
9565.5-3488.2
|
First derivative
|
5
|
0.90
|
83.24
|
0.691
|
91.87
|
Fat
|
9565.5-3488.2
|
First derivative
|
7
|
2.22
|
82.82
|
1.43
|
93.89
|
Protein
|
9565.5-3488.2
|
First derivative
|
1
|
0.765
|
96.6
|
0.736
|
97.04
|
FFA
|
12378.9-3594.8
|
Second derivative
|
5
|
0.738
|
95.97
|
0.579
|
97.96
|
PV %
|
12378.9-3594.8
|
Second derivative
|
7
|
1.36
|
85.06
|
0.845
|
95.68
|
x Root mean square error of cross validation |
y Root mean square error of estimation |
3.4 Comparative evaluation of FT-NIR and chemical analysis
The developed FTNIR procedure was validated by computing the RPD which is the standard deviation (SD) of the reference set with the standard error of prediction. Generally, RPD 2.5-3.0 (coarse) and 3.1–4.9 (fine) indicate the separating of the samples. A RPD value of 5.0–8.0 are suitable for quality and process analysis, and a RPD more than 8.0 is relevant for broad applications (Williams 2001; Ferreira et al. 2014). The maximum RPD value were noticed in FTNIR method for FFA (41.27), moisture content (34.92), and PV (29.49) indicating good definiteness and model performance in prediction of the concerned value. More RPD variability for different peanut sample was due to the composition of peanut for different process parameters. The efficacy of the developed FT-NIR method for quality testing i.e. moisture content, fat, protein, FFA and PV value of peanut were verified with the chemical testing of unknown (3 set of different peanut samples) samples of peanut. In this validation, the FTNIR spectra of 2 sets of peanut (fresh and spoiled) were taken and was taken for determination of its moisture content, protein, fat, PV value and FFA content by using the standard chemical methods. Both the chemical and FTNIR predicted findings were compared with paired sample t-test by using the statistically tool (SPSS, 22.0) and the results are shown in Table 2. There was no statistical significant differences in the means of FTNIR and laboratory standard methods. Thus, it signifies that the application of the FTNIR method developed over the analytical ones can save time as well as cost for the determination of peanut quality before it is further processed and it also leave no chemical residues as in the laboratory standard methods.
Table 2
Different quality parameters of fresh and spoiled peanut samples obtained through rapid FTNIR method and lab analytical methods
Sample
|
Parameter
|
FTNIR
|
Analytical methods
|
|
|
Avg
|
SD
|
RSD
|
RPD
|
Error
(%)
|
Accuracy (%)
|
Avg
|
SD
|
RSD
|
RPD
|
Error (%)
|
Accuracy (%)
|
Fresh peanut
|
Moisture
|
6.27
|
0.03
|
0.46
|
34.92
|
0.00
|
100.00
|
6.19
|
0.07
|
1.20
|
13.48
|
0.01
|
99.99
|
Fat
|
45.43
|
0.31
|
0.68
|
0.99
|
0.31
|
99.69
|
46.03
|
0.22
|
0.47
|
0.25
|
0.86
|
99.14
|
Protein
|
26.37
|
1.43
|
5.41
|
14.91
|
0.10
|
99.90
|
26.17
|
0.93
|
3.55
|
19.87
|
0.05
|
99.95
|
PV %
|
0.26
|
0.03
|
13.04
|
29.49
|
0.00
|
100.00
|
0.25
|
0.01
|
4.49
|
87.71
|
0.00
|
100.00
|
FFA
|
1.80
|
0.02
|
1.35
|
41.27
|
0.00
|
100.00
|
1.76
|
0.04
|
2.14
|
26.54
|
0.00
|
100.00
|
Spoiled peanut
|
Moisture
|
14.89
|
0.74
|
4.95
|
1.36
|
0.54
|
99.46
|
14.62
|
0.43
|
2.93
|
2.33
|
0.18
|
99.82
|
Fat
|
40.65
|
0.95
|
2.34
|
0.20
|
4.72
|
95.28
|
40.85
|
0.59
|
1.45
|
0.32
|
1.83
|
98.17
|
Protein
|
24.66
|
2.17
|
8.81
|
2.41
|
0.90
|
99.10
|
23.66
|
1.35
|
5.71
|
3.84
|
0.35
|
99.65
|
PV %
|
15.75
|
0.67
|
4.27
|
1.49
|
0.45
|
99.55
|
15.70
|
0.64
|
4.07
|
1.56
|
0.41
|
99.59
|
FFA
|
9.48
|
0.11
|
1.18
|
8.96
|
0.01
|
99.99
|
9.34
|
0.11
|
1.14
|
9.40
|
0.01
|
99.99
|
tcal = 0.029; tcri = 2.31; α = 0.05 |